Introduction to R

R is a programming language for statistics. When working with large data sets, it enables the manipulation of data and creation of graphical representations.

This interactive course in workshop format introduces the basics of programming in R. Participants will follow a live demonstration of command execution on their own computers and work individually on exercises covering the topics listed in the learning goals.

Learning Goals

Participants should be able to:

• Work in R Studio (a graphical user interface for the programming language R)

• Grasp the basic concepts of programming such as variables and functions

• Be able to control the flow of a program

• Import, manipulate, visualise and export data

• Perform basic statistical tests on data

Time and Place

Online at the IMB Bioinformatics Slack

Session 1: Tuesday 4-5pm 21.04.20

Required Materials

See Software Installation

Reference Materials

Introductory Statistics with R, Peter Dalgaard

Contact

Chris Field

Session 1

• R Studio
  • Useful commands
  • Other Tips
• Calculation and Variables
  • R as a calculator
  • Variables
  • Exercises
  • Types
  • Mode detection
  • Vectors
  • Exercises

Session 2

• Lists and Data Frames
  • Lists
  • Data Frames
  • Exercises
  • Importing Data
  • Exporting Data
  • Exercises
• Names and Indexing
  • Names
  • Indexing
  • Exercises
  • Logical Slicing
  • Exercises

Session 3

• Functions
  • Basic functions in R
  • Exercises 1
  • Functions 2
  • Exercises 2
  • Functions and class
  • Introduction to statistical functions in R
  • Exercise 3
• Introduction to linear regression
  • Linear Regression
  • Basic Plotting
  • Exercises

Session 4

• Program Flow
  • The if function
  • Exercises
  • The for loop
  • Exercises
  • The *apply functions
  • Exercise
• Writing Functions
  • Introduction
  • Defining a basic function
  • Organising Functions
  • Exercises

Session 5

• Packages
  • Loading a Package
  • Installing a Package
  • Bioconductor
• Case Study: Analysis of mOTUs Output in R
  • Q1. How does metagenomic data compare with metatranscriptomic data?
  • Q2. Is there structure in the data?
    • Ordination
    • Hierarchical Clustering
    • Challenge

Session 6

• Data Transformation with tidyverse
  • Tibbles instead of data frames
  • The pipe
  • Arrange data
  • Select variables
  • Filter observations
  • Create new variables
  • Summarise data
  • Pivot data
  • Join data
  • Exercises
• Data visualization with ggplot2
  • Aesthetics
  • Geometric objects
  • Other layers
  • Exercises